Extreme Face Inpainting with Sketch-Guided Conditional GAN
This addresses face inpainting for applications like photo restoration, but it is incremental as it builds on existing conditional GAN methods.
The paper tackles the problem of recovering severely damaged face images by using a conditional GAN guided by structural edges, achieving effective inpainting for extreme cases with large masked regions.
Recovering badly damaged face images is a useful yet challenging task, especially in extreme cases where the masked or damaged region is very large. One of the major challenges is the ability of the system to generalize on faces outside the training dataset. We propose to tackle this extreme inpainting task with a conditional Generative Adversarial Network (GAN) that utilizes structural information, such as edges, as a prior condition. Edge information can be obtained from the partially masked image and a structurally similar image or a hand drawing. In our proposed conditional GAN, we pass the conditional input in every layer of the encoder while maintaining consistency in the distributions between the learned weights and the incoming conditional input. We demonstrate the effectiveness of our method with badly damaged face examples.